Use of Past Typhoon Data for Landslide Modeling and Mapping

Karl ChangAdjunct Professor of Geography

National Taiwan University

Landslides occur when unstable rock and soil masses on slopes are disturbed by intense storms, earthquakes, human activities (e.g., road construction), or a combination of these factors.

Many shallow landslides (within a depth of 2 or 3 m) in Taiwan are triggered by typhoons during the summer months. Each typhoon produces a different rainfall distribution, depending on its track and position and atmospheric conditions, and makes it difficult to predict the location and size of landslides it triggers.

This talk covers two areas:1. How to incorporate past rainfall data in a landslide

susceptibility model to improve its performance in predicting landslide occurrence

2. How to use landslide data from past typhoons for mapping landslides triggered by a new typhoon

Landslide Modeling

Can the addition of typhoon rainfall data improve the performance of a landslide susceptibility model in its prediction?

Can we combine the information on past typhoon events for predicting landslides triggered by a new typhoon?

K. Chang, S. Chiang, Y. Chen, and A.C. Mondini. 2014. Modeling the spatial occurrence of shallow landslides triggered by typhoons. Geomorphology 208: 137-148.

K.W.: KaopingWatershed

The integrated model:

where y is the dependent variable representing the presence or absence of a landslide; a is a constant; bi is the i-th regression coefficient; x1 is the difference between the estimated maximum 24-h rainfall intensity and the critical rainfall* value (RID) at a location; and x2 is the estimated rainfall duration at the location.

)exp(1

)exp(

2211

2211

xbxba

xbxbap

xb xb a (y) logit 2211

K. Chang and S. Chiang. 2009. An integrated model for predicting rainfall-induced landslides. Geomorphology 105: 366-373.

Typhoon event

DateCWB Typhoon track category

Min.cumul. rainfall

Max.cumul. rainfall

Ave.cumul. rainfall

Toraji 2001(7/28 – 7/31) 3 15 749 304

Nari 2001(9/13 – 9/19) unclassifed 9 516 309

Mindulle 2004(6/28 – 7/03) 6 399 2115 1004

Haitang 2005(7/16 – 7/20) 3 542 2184 1221

Bilis 2006(7/12 – 7/15) 2 319 963 607

Sepat 2007(8/16 – 8/19) 3 198 1133 691

Kalmaegi 2008(7/16 – 7/18) 2 343 900 568

Sinlaku 2008(9/11 – 9/16) 2 138 1006 513

Morakot 2009(8/05 – 8/10) 3 770 2959 1803

Minimum, maximum, and average cumulative rainfall at 24 gauging stations by typhoon in the Kaoping watershed

The integrated models for the nine typhoon events are all significant at the 0.05 level. The area under the curve (AUC) values range from 0.68 to 0.85, with an average of 0.77. These AUC values represent good to excellent predictive capabilities of the models.

To test the possibility of using the past typhoon data for prediction, we experimented with “catch-all” and “group” models. A catch-all model uses information on all available typhoons, and a group model uses information on a group of typhoons with similar rainfall characteristics. We applied the cross-validation technique to test the predictability of the models.

Validated Typhoon AUC Overallaccuracy

MSR Proportion of landslide areas (numbers) correctly

predicted, in %Catch-all model

Toraji 0.73 0.81 0.60 28 (40)Nari 0.73 ~1.0 0.50 ~0 (~0)Mindulle 0.73 0.97 0.61 10 (25)Haitang 0.69 0.84 0.80 63 (75)Bilis 0.73 0.97 0.53 3 (8)Sepat 0.73 0.99 0.59 10 (18)Kalmaegi 0.73 0.55 0.70 77 (85)Sinlaku 0.73 ~1.0 0.50 ~0 (~0)Morakot 0.77 0.52 0.72 79 (94)

Group 1 ModelMindulle 0.74 0.99 0.55 2 (10)Haitang 0.71 0.98 0.58 11 (17)Morakot 0.84 0.45 0.70 85 (96)

Group 2 Model

Bilis 0.80 0.86 0.64 23 (42)

Sepat 0.76 0.60 0.77 79 (94)

Kalmaegi 0.71 0.59 0.71 76 (82)

Sinlaku 0.76 0.73 0.73 22 (74)

Cross-validation results of the catch-all model and group models

Landslide Modeling: Summary

The integrated method provides a mechanism by which a catch-all or group model can be developed from information on past typhoon events to predict landslides triggered by a new typhoon.

For future applications of the integrated method, we need to have good-quality rainfall and geoenvironmental data; and a large, representative landslide inventory.

Landslide Mapping

Landslide can be treated as a land cover type, and landslides can be mapped by using semi-automatic or automatic image analysis. One of the mainstays of quantitative image analysis since the 1970s is the pixel-based, single image, supervised Maximum Likelihood (ML) classification.

ML requires training samples, and their preparation is highly demanding on both resources and time and can also introduce subjectivity.

A New Landslide Mapping Method

Our study proposes to use an independent set of training samples in image analysis, which is pre-prepared from images on previous landslide events.

If this approach turns out to be reliable, the training samples can be saved in a digital library and used routinely to obtain a new landslide map once a new image is available on the landslide affected area.

To demonstrate its feasibility, we have applied the method to mapping shallow landslides triggered by typhoons in southern Taiwan.

To test its performance, we compare the classified landslide maps with landslide maps prepared through manual interpretation of the same test images.

Mondini, A. C. 2016. Monte Carlo approach to simulate spectral fingerprints for automatic event landslides mapping. Submitted

Our method includes three major steps:

1. Create the spectral fingerprints from one or more past images with the presence of fresh landslides that have been pre-processed (i.e., geometric and radiometric corrections).

2. Calculate the class membership probability P(LP)*, as well as its uncertainty (% relative error)*, of the landslide class using the spectral fingerprints and random numbers (a Monte Carlo method).

𝑝 𝒙 𝑤𝑖 = (2𝜋)−𝑁/2|Σ𝑖|

−1/2𝑒𝑥𝑝 −1

2(𝒙 − 𝝁𝑖)

𝑡Σ𝑖−1 𝒙 − 𝝁𝑖

𝛿𝑝 𝑤𝑖 𝒙 =1

1𝑀 𝑝 𝒙 𝑤𝑖

−𝑝 𝒙 𝑤𝑖 1𝑀 𝑝 𝒙 𝑤𝑖

2𝛿𝑝 𝒙 𝑤𝑖

2

+ 𝑘

𝑝 𝒙 𝑤𝑖 1𝑀 𝑝 𝒙 𝑤𝑖

2𝛿𝑝 𝒙 𝑤𝑛

2

3. Introduce external geo-environmental variables through a landslide susceptibility model as a filter, P(LO), to obtain the final landslide probability map P(L). P(L)= P(LP) x P(LO).

Acquisition

time

Incidence angle Coordinate

system

Formosat 2-1 2005/08/01

01:52:49

6.77 TWD97

Formosat 2-2 2009/08/24

01:42:55

3.885 UTM-WGS84

Formosat 2-3 2009/08/19

01:42:34

6.57 UTM-WGS84

Formosat 2-4 2009/08/17

01:42:36

5.68 UTM-WGS84

Image 1 is pre-Morakot (August 2009), whereas images 2, 3, and 4 are post-Morakot. Spectral fingerprints are prepared from images 3 and 4. Images 1 and 2 are used for comparison between classified and manually prepared landslide maps.

Image 1

Classified landslide map

Manually prepared landslide map

Image 2

Classified landslide map

Manually prepared landslide map

PI1 Vs SA1 PI2 Vs SA2

%

Commission

%

Omission

Kappa

Coeff.

%

Commission

%

Omission

Kappa

Coeff.

Non-

landslide

0.154 0.341 0.78 3.72 3.57 0.615

Landslide 38.0 21.6 0.62 33.81 34.76 0.625

Landslide Mapping: Summary

The results from the case studies show that the new landslide mapping method performed well in mapping landslides triggered by two different typhoons in the same area using FORMOSAT 2 images, and it is possible to apply the method to other areas with similar characteristics and using other sensors.

Conclusion

Past typhoon data, including rainfall and landslides, are useful for landslide susceptibility modeling and landslide mapping. It is therefore important to create and maintain a database on past typhoon data.